Differentiating between data-mining and text-mining terminology

Abstract

When a new discipline emerges, it usually takes some time and a great deal of academic discussion before concepts and terms become standardized. Text mining is one such new discipline. In a groundbreaking article, Untangling text data mining, Hearst tackled the problem of clarifying text-mining concepts and terminology. This article, a conceptual study, is aimed at building on Hearst's ideas by pointing out some inconsistencies and suggesting an improved and extended categorization of data-mining and text-mining techniques. A brief overview is given of the problems regarding text-mining concepts. This is followed by a summary and critical discussion of Hearst's attempt to clarify the terminology. The essence of text mining is found to be the discovery or creation of new knowledge from a collection of documents. The parameters of non-novel, semi-novel and novel investigation are used to differentiate between full-text information retrieval, standard text mining and intelligent text mining. The same parameters are also used to differentiate between related processes for numerical data and text metadata. These distinctions may be used as a road map in the evolving fields of data/information retrieval, knowledge discovery and the creation of new knowledge.